Easily Created Prediction Model Using Automated Artificial Intelligence Framework (Prediction One, Sony Network Communications Inc., Tokyo, Japan) for Subarachnoid Hemorrhage Outcomes Treated by Coiling and Delayed Cerebral Ischemia

Katsuki, Masahito; Kawamura, Shin; Koh, Akihito

doi:10.7759/cureus.15695

Cited by 9 publications

(8 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The clinical characteristics of the 559 stroke patients (421 CI, 98 ICH, 40 SAH; 224 women and 335 men) are summarized in table 1. The median (interquartile range) age was 77 (69-83), and LOS in the acute care ward 15 (7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26). Regarding discharge destinations, 205 patients were discharged home, 122 patients were transferred to KRW, 81 nursing facilities, 79 long-term hospitals, and 72 patients died.…”

Section: Clinical Characteristicsmentioning

confidence: 99%

“…We hypothesized that we could make a good prediction model for our hospital using the DL framework, even with a small dataset. Therefore, we herein produced the prediction model using DL framework, Prediction One (Sony Network Communications Inc., Tokyo, Japan, https://predictionone.sony.biz/) [19][20][21]24,25] with our dataset and compared the utility of the DLbased model to previously-reported multiple regression models. We also investigated our patients' characteristics admitted to KRW because our hospital is unique in that KRW is annexed to an acute care hospital.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Deep Learning-Based Functional Independence Measure Score Prediction After Stroke in Kaifukuki (Convalescent) Rehabilitation Ward Annexed to Acute Care Hospital

Katsuki¹,

Narita²,

Ozaki³

et al. 2021

Cureus

Self Cite

View full text Add to dashboard Cite

IntroductionPrediction models of functional independent measure (FIM) score after kaifukuki (convalescent) rehabilitation ward (KRW) are needed to decide the treatment strategies and save medical resources. Statistical models were reported, but their accuracies were not satisfactory. We made such prediction models using the deep learning (DL) framework, Prediction One (Sony Network Communications Inc., Tokyo, Japan). MethodsOf the 559 consecutive stroke patients, 122 patients were transferred to our KRW. We divided our 122 patients' data randomly into halves of training and validation datasets. Prediction One made three prediction models from the training dataset using (1) variables at the acute care ward admission, (2) those at the KRW admission, and (3) those combined (1) and (2). The models' determination coefficients (R 2 ), correlation coefficients (rs), and residuals were calculated using the validation dataset. ResultsOf the 122 patients, the median age was 71, length of stay (LOS) in acute care ward 23 (17-30) days, LOS in KRW 53 days, total FIM scores at the admission of KRW 85, those at discharge 108. The mean FIM gain and FIM efficiency were 19 and 0.417. All patients were discharged home. Model (1), (2), and (3)'s R 2 were 0.794, 0.970, and 0.972. Their mean residuals between the predicted and actual total FIM scores were -1.56±24.6, -4.49±17.1, and -2.69±15.7. ConclusionOur FIM gain and efficiency were better than national averages of FIM gain 17.1 and FIM efficiency 0.187. We made DL-based total FIM score prediction models, and their accuracies were superior to those of previous statistically calculated ones. The DL-based FIM score prediction models would save medical costs and perform efficient stroke and rehabilitation medicine.

show abstract

Section: Clinical Characteristicsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Functional Independence Measure Score Prediction After Stroke in Kaifukuki (Convalescent) Rehabilitation Ward Annexed to Acute Care Hospital

Katsuki¹,

Narita²,

Ozaki³

et al. 2021

Cureus

Self Cite

View full text Add to dashboard Cite

show abstract

“…SONY Prediction One (Sony Network Communications Inc.), a predictive analysis software with validity and consistency ensured by the previous studies, has advantages over other statistical analysis software (Katsuki et al, 2021; Sony Network Communications Inc., 2022; Watanabe et al, 2021). It eliminates preconceptions, contributes to finding overlooked factors, and enables the analysis of influencing factors that are unpredictable and cannot be explained by previous hypotheses.…”

Section: Introductionmentioning

confidence: 99%

Prediction models for the impact of the COVID‐19 pandemic on research activities of Japanese nursing researchers using deep learning

Lee

Takahashi

Kawasaki

et al. 2023

Japan Journal Nursing Sci

View full text Add to dashboard Cite

Aim This study aimed to construct and evaluate prediction models using deep learning to explore the impact of attributes and lifestyle factors on research activities of nursing researchers during the COVID‐19 pandemic. Methods A secondary data analysis was conducted from a cross‐sectional online survey by the Japanese Society of Nursing Science at the inception of the COVID‐19 pandemic. A total of 1089 respondents from nursing faculties were divided into a training dataset and a test dataset. We constructed two prediction models with the training dataset using artificial intelligence (AI) predictive analysis tools; motivation and time were used as predictor items for negative impact on research activities. Predictive factors were attributes, lifestyle, and predictor items for each other. The models' accuracy and internal validity were evaluated using an ordinal logistic regression analysis to assess goodness‐of‐fit; the test dataset was used to assess external validity. Predicted contributions by each factor were also calculated. Results The models' accuracy and goodness‐of‐fit were good. The prediction contribution analysis showed that no increase in research motivation and lack of increase in research time strongly influenced each other. Other factors that negatively influenced research motivation and research time were residing outside the special alert area and lecturer position and living with partner/spouse and associate professor position, respectively. Conclusions Deep learning is a research method enabling early prediction of unexpected events, suggesting new applicability in nursing science. To continue research activities during the COVID‐19 pandemic and future contingencies, the research environment needs to be improved, workload corrected by position, and considered in terms of work‐life balance.

show abstract

“… 2 Delayed cerebral ischemia (DCI) is a very common adverse event, which is associated with a poor outcome 3 and whose prediction is difficult. 4 Secondary brain injury following aSAH involves some crucial pathophysiological processes, including immune activation, subsequent inflammation reaction and final neuronal death. 5 There is robust evidence showing that microglia are responsible for the innate immune response in the brain and participate in aSAH-induced neuroinflammation.…”

Section: Introductionmentioning

confidence: 99%

Serum Soluble Scavenger Receptor A Levels are Associated with Delayed Cerebral Ischemia and Poor Clinical Outcome After Aneurysmal Subarachnoid Hemorrhage: A Prospective Observational Study

Jiang¹,

Chen²,

Hu³

et al. 2022

NDT

View full text Add to dashboard Cite

Objective Scavenger receptor A (SRA), a pattern recognition molecule, is implicated in immune response after acute brain injury. We strived to identify serum soluble SRA (sSRA) as a potential biomarker of prognosis after aneurysmal subarachnoid hemorrhage (aSAH). Methods In this prospective observational study, we quantified serum sSRA levels of 131 aSAH patients and 131 healthy controls. A poor outcome was defined as extended Glasgow outcome scale (GOSE) scores of 1–4 at 90 days after injury. Relations of serum sSRA levels to severity, delayed cerebral ischemia (DCI) and poor outcome were assessed using multivariate analysis. Predictive efficiency was determined via area under receiver operating characteristic curve (AUC). Results Serum sSRA levels were markedly higher in aSAH patients than in controls (median, 2.9 ng/mL versus 1.0 ng/mL; P < 0.001). Serum sSRA levels were independently correlated with Hunt-Hess scores (beta, 0.569; 95% confidence interval (CI), 0.244–0.894; P = 0.001), modified Fisher scores (beta, 0.664; 95% CI, 0.254–1.074; P = 0.002) and 90-day GOSE scores (beta, −0.275; 95% CI, −0.440–0.110; P = 0.005). Serum sSRA levels independently predicted DCI (odds ratio, 1.305; 95% CI, 1.012–1.687; P = 0.040) and a poor outcome (odds ratio, 2.444; 95% CI, 1.264–4.726; P = 0.008), as well as showed significant accuracy for the discrimination of DCI (AUC, 0.753; 95% CI, 0.649–0.857; P < 0.001) and a poor outcome (AUC, 0.800; 95% CI, 0.721–0.880; P < 0.001). Its combination with Hunt-Hess scores and modified Fisher scores displayed significantly improved AUCs for predicting DCI and poor outcome, as compared to any of them (all P < 0.05). Conclusion There is a significant elevation of serum sSRA levels after aSAH, which in close correlation with illness severity, are independently associated with DCI and poor clinical outcome after aSAH. Hypothetically, SRA may regulate immune response in acute brain injury after aSAH and serum sSRA is presumed to be a potential prognostic biomarker of aSAH.

show abstract

Easily Created Prediction Model Using Automated Artificial Intelligence Framework (Prediction One, Sony Network Communications Inc., Tokyo, Japan) for Subarachnoid Hemorrhage Outcomes Treated by Coiling and Delayed Cerebral Ischemia

Cited by 9 publications

References 45 publications

Deep Learning-Based Functional Independence Measure Score Prediction After Stroke in Kaifukuki (Convalescent) Rehabilitation Ward Annexed to Acute Care Hospital

Deep Learning-Based Functional Independence Measure Score Prediction After Stroke in Kaifukuki (Convalescent) Rehabilitation Ward Annexed to Acute Care Hospital

Prediction models for the impact of the COVID‐19 pandemic on research activities of Japanese nursing researchers using deep learning

Serum Soluble Scavenger Receptor A Levels are Associated with Delayed Cerebral Ischemia and Poor Clinical Outcome After Aneurysmal Subarachnoid Hemorrhage: A Prospective Observational Study

Contact Info

Product

Resources

About